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样本高效增强学习控制器用于帕金森病的深度大脑刺激.
Harsh Ravivarapu1, Gaurav Bagwe1, Xiaoyong Yuan1
1Department of Electrical and Computer Engineering, Clemson University, Clemson, SC.
ArXiv
|July 17, 2025
概括
SEA-DBS是一种新的强化学习框架,增强了对帕金森病的自适应性深度大脑刺激 (aDBS). 它通过提高样本效率和用于神经调节的探索稳定性来提供高效,个性化的控制.
科学领域:
- 神经科学是一个神经科学.
- 生物医学工程 生物医学工程
- 计算神经科学是一种神经科学.
背景情况:
- 深度大脑刺激 (DBS) 是帕金森病 (PD) 的标准治疗方法,但目前的系统并非适应性或个性化.
- 适应式DBS (aDBS) 使用β频段振荡等生物标志物进行动态刺激控制.
- 强化学习 (RL) 提供了个性化aDBS的潜力,但在样本效率和硬件部署方面面临挑战.
研究的目的:
- 引入SEA-DBS,这是一个新的演员批判框架,旨在克服基于RL的自适应神经刺激的局限性.
- 为了提高样本效率,探索稳定性和硬件兼容性,用于资源受限的神经调制.
主要方法:
- 开发了一个采样效率高的演员-批评框架 (SEA-DBS),集成了一个预测奖励模型.
- 实施了基于Gumbel-Softmax的探索,以在二元行动空间中实现稳定,可差异化的政策更新.
- 通过对帕金森氏症基本腺活动的生物现实的模拟来评估SEA-DBS.
主要成果:
- 与现有的RL方法相比,SEA-DBS表现出更快的趋同.
- 在模拟中实现了对病态β频段振荡的更强的抑制.
- 展示了对培训后FP16量子化的弹性,表明了硬件兼容性.
结论:
- SEA-DBS介绍了一个实用和有效的基于RL的框架,用于实时,资源受限的自适应性深度大脑刺激.
- 拟议的方法解决了将RL应用于帕金森病适应性神经刺激的关键挑战.
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